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Data-Driven Methods for Adaptive Spoken Dialogue Systems: - download pdf or read online

By Oliver Lemon (auth.), Oliver Lemon, Olivier Pietquin (eds.)

ISBN-10: 1461448026

ISBN-13: 9781461448020

ISBN-10: 1461448034

ISBN-13: 9781461448037

Data pushed tools have lengthy been utilized in automated Speech acceptance (ASR) and Text-To-Speech (TTS) synthesis and feature extra lately been brought for discussion administration, spoken language knowing, and average Language iteration. computing device studying is now current “end-to-end” in Spoken discussion structures (SDS). in spite of the fact that, those options require facts assortment and annotation campaigns, which are time-consuming and costly, in addition to dataset growth by means of simulation. during this e-book, we offer an outline of the present kingdom of the sector and of modern advances, with a selected concentrate on adaptivity.

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Computational Linguistics 19(2), 313–330 (1993) 22. : Semantic parsing for high-precision semantic role labelling. In: Proceedings of the 20th Conference on Computational Natural Language Learning (CoNLL 2008), Manchester, UK (2008) 23. : Spoken Language Understanding in dialogue systems, using a 2-layer Markov Logic Network: Improving semantic accuracy. In: Proceedings of Londial, 2008 24. : The proposition bank: An annotated corpus of semantic roles. Computational Linguistics 31(1), 71–106 (2005) 25.

G. J. Henderson and F. Jurˇc´ıcˇ ek 26 PLACE NAME = main square). After parsing, a deterministic algorithm recovers the original values for category labels, which is detailed in [20]. The learning algorithm consists of the following steps: • Input: a set of (utterance, semantic tree) pairs, a domain database • Output: a classifier for the semantic tree of the input utterance 1. Substitute database values in the training utterances with category labels. 2. Assign initial semantics to each utterance.

Evaluating semantic-level confidence scores with multiple hypotheses. In: Proceedings of the Ninth Conference of the International Speech Communication Association (INTERSPEECH 2008), pp. 1153–1156. Brisbane, Australia (2008) 29. : User study of the Bayesian update of dialogue state approach to dialogue management. In: Proceedings of Interspeech, 2008 30. : Understanding spontaneous speech: the phoenix system. In: Proceedings of ICASSP, vol. 1, pp. 365–367, 1991 31. : Applying POMDPs to Dialog Systems in the Troubleshooting Domain.

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Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces by Oliver Lemon (auth.), Oliver Lemon, Olivier Pietquin (eds.)

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